Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A method for time and sentiment based messaging, comprising: obtaining, by a server, information for a set of messages from online social networks related to a specified object, the information comprising at least a user identifier associated with each message, a time of each message, and content of each message; for each unique identifier, establishing, by the server, an initial message from the set of messages related to the specified object; analyzing, by the server, each message in the set of messages to determine a sentiment of each message toward the specified object; building, by the server, a sentiment time line for each unique user identifier using the sentiment of each message toward the specified object; building, by the server, a time-based sentiment model related to the specified object by overlapping the sentiment time lines for each unique user identifier according to the initial message for each unique user identifier; identifying, by the server, a sentiment inflection point in the time-based sentiment model, the sentiment inflection point representing a change in the sentiment toward the specified object; building, by the server, a new sentiment time line for an additional unique user identifier; overlapping, by the server, the new sentiment time line with the time-based sentiment model according to an initial message for the additional unique user identifier; predicting, by the server, a change in the sentiment related to the specified object by the additional unique user identifier based on the new sentiment time line, the time-based sentiment model, and the sentiment inflection point; and generating a message targeting the predicted change in the sentiment related to the specified object by the additional unique user identifier.
This invention relates to analyzing and predicting sentiment changes in online social network messages about a specified object, such as a product, brand, or topic. The method involves collecting messages from social networks, including user identifiers, timestamps, and message content. For each user, an initial message about the object is identified, and sentiment analysis is performed on all messages to determine their emotional tone. Sentiment timelines are constructed for each user, showing how their sentiment evolves over time. These timelines are then combined into a time-based sentiment model, which visualizes collective sentiment trends and identifies inflection points where sentiment shifts significantly. When a new user's messages are analyzed, their sentiment timeline is integrated into the model to predict future sentiment changes. Based on these predictions, targeted messages are generated to influence or respond to the anticipated sentiment shift. The system helps businesses or organizations understand and manage public perception by leveraging historical sentiment patterns and real-time data.
2. The method of claim 1 , wherein the establishing of the initial message from the set of messages related to the specified object for each unique identifier comprises: identifying the unique user identifiers in the set of messages and organizing the set of messages according to the unique user identifiers; normalizing the set of messages based on the time of each message in the set; and for each unique user identifier, establishing the initial message.
This invention relates to organizing and analyzing message data associated with a specified object, such as a product, service, or topic, to identify initial messages from users. The problem addressed is the difficulty in tracking the first interaction or message from each user regarding a specific object within a large dataset of messages, which is crucial for understanding user engagement, sentiment, or behavior over time. The method involves processing a set of messages related to a specified object by first identifying unique user identifiers within the dataset. These messages are then organized according to the unique identifiers, grouping all messages from the same user together. The messages are further normalized based on their timestamps to ensure chronological accuracy. For each unique user identifier, the initial message is established by selecting the earliest message in the normalized sequence. This allows for the extraction of first impressions, initial feedback, or early interactions from each user, which can be valuable for analytics, customer insights, or trend analysis. The technique ensures that the initial message for each user is accurately identified, even in large or unstructured datasets, by systematically filtering, grouping, and time-ordering the messages. This approach is particularly useful in applications like social media monitoring, customer support analysis, or market research, where understanding the first point of contact or interaction is critical.
3. The method of claim 1 , wherein the time-based sentiment model relates to the specified object over a lifecycle of the specified object.
This invention relates to sentiment analysis systems that track and analyze sentiment data over time, specifically for objects with a defined lifecycle. The technology addresses the challenge of understanding how public or user sentiment evolves regarding a particular object, such as a product, service, or brand, throughout its existence. Traditional sentiment analysis often captures static snapshots of sentiment, failing to account for temporal changes that may influence decision-making, marketing strategies, or product development. The method involves using a time-based sentiment model that dynamically tracks sentiment data associated with a specified object over its entire lifecycle. This model processes sentiment data collected at different time points, allowing for the identification of trends, shifts, and patterns in sentiment as the object progresses through stages such as development, launch, growth, and decline. The model may incorporate machine learning techniques to weigh the influence of time on sentiment, ensuring that older data does not disproportionately affect current sentiment assessments. By analyzing sentiment in this time-sensitive manner, the system provides insights into how external factors, such as market conditions or user feedback, impact perceptions of the object over time. This enables stakeholders to make informed decisions based on evolving sentiment trends rather than isolated data points. The approach is particularly useful for industries where product or service performance is subject to dynamic public opinion, such as consumer electronics, entertainment, or social media platforms.
4. The method of claim 1 , wherein the sentiment inflection point is a point in a lifecycle of the specified object when sentiments toward the specified object change.
This invention relates to analyzing sentiment trends over time for a specified object, such as a product, service, or brand, to identify key moments when public perception shifts. The method involves tracking sentiment data associated with the object across multiple time intervals, where sentiment is measured using natural language processing techniques applied to text data like reviews, social media posts, or news articles. The system processes this data to detect patterns and determine a sentiment inflection point—a specific moment in the object's lifecycle when overall sentiment changes significantly, either positively or negatively. This inflection point may correspond to events like product launches, controversies, or marketing campaigns. The method further includes visualizing sentiment trends and inflection points to help stakeholders understand how public opinion evolves and make informed decisions. The analysis may also incorporate contextual factors, such as external events or competitor actions, to provide deeper insights into sentiment shifts. By identifying these critical points, businesses can adjust strategies to mitigate negative sentiment or capitalize on positive trends. The system may also predict future sentiment trajectories based on historical data and current trends.
5. A computer program product for time and sentiment based messaging, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: obtain information for a set of messages from online social networks related to a specified object, the information comprising at least a user identifier associated with each message, a time of each message, and content of each message; for each unique identifier, establish an initial message from the set of messages related to the specified object; analyze each message in the set of messages to determine a sentiment of each message toward the specified object; build a sentiment time line for each unique user identifier using the sentiment of each message toward the specified object; build a time-based sentiment model related to the specified object by overlapping the sentiment time lines for each unique user identifier according to the initial message for each unique user identifier; identify a sentiment inflection point in the time-based sentiment model, the sentiment inflection point representing a change in the sentiment toward the specified object; build a new sentiment time line for an additional unique user identifier; overlap the new sentiment time line with the time-based sentiment model according to an initial message for the additional unique user identifier; predict a change in the sentiment related to the specified object by the additional unique user identifier based on the new sentiment time line, the time-based sentiment model, and the sentiment inflection point; and generate a message targeting the predicted change in the sentiment related to the specified object by the additional unique user identifier.
This invention relates to a system for analyzing and predicting sentiment changes in online social network messages related to a specified object, such as a product, brand, or topic. The system collects messages from social networks, extracting user identifiers, timestamps, and message content. For each user, it identifies their initial message about the object and analyzes the sentiment of all their subsequent messages to build a sentiment timeline. These timelines are then aggregated into a time-based sentiment model, which reveals sentiment inflection points—key moments where collective sentiment shifts. When a new user engages with the object, their sentiment timeline is overlaid onto the existing model to predict how their sentiment may evolve based on historical patterns. The system then generates targeted messages designed to influence the predicted sentiment change. The approach enables dynamic, data-driven engagement strategies by leveraging temporal sentiment trends and individual user behavior.
6. The computer program product of claim 5 , wherein the establishing of the initial message from the set of messages related to the specified object for each unique identifier comprises: identify the unique user identifiers in the set of messages and organizing the set of messages according to the unique user identifiers; normalize the set of messages based on the time of each message in the set; and for each unique user identifier, establish the initial message.
This invention relates to organizing and processing digital messages, particularly for identifying and establishing initial messages in a set of messages related to a specified object. The problem addressed is efficiently categorizing and normalizing messages from multiple users to determine the first message in each user's sequence, which is useful for applications like chat analysis, customer support tracking, or social media monitoring. The system identifies unique user identifiers within a set of messages and organizes the messages according to these identifiers. The messages are then normalized based on their timestamps to ensure chronological order. For each unique user identifier, the system establishes the initial message by selecting the earliest message in the normalized sequence. This process helps in tracking the origin of conversations or interactions, improving data analysis and user engagement metrics. The invention ensures accurate identification of the first message in each user's interaction with a specified object, which is critical for applications requiring chronological tracking of user activities. The normalization step ensures that messages are processed in the correct order, even if they are received out of sequence or have inconsistent timestamps. This method enhances the reliability of message-based analytics and user behavior analysis.
7. The computer program product of claim 5 , wherein the time-based sentiment model relates to the specified object over a lifecycle of the specified object.
This invention relates to sentiment analysis systems that track and analyze sentiment data over time for a specified object, such as a product, service, or brand. The technology addresses the challenge of understanding how public perception evolves throughout the lifecycle of an object, from initial release to long-term use. Traditional sentiment analysis often provides static snapshots rather than dynamic insights, limiting the ability to detect trends, shifts, or critical events. The system includes a time-based sentiment model that processes sentiment data collected from various sources, such as social media, reviews, or customer feedback, over the entire lifecycle of the object. The model captures temporal patterns, allowing users to identify sentiment fluctuations, peak engagement periods, or declining interest. By analyzing sentiment trends over time, businesses can make data-driven decisions, such as adjusting marketing strategies, improving product features, or addressing customer concerns proactively. The model may incorporate machine learning techniques to refine sentiment predictions and adapt to changing language patterns or emerging topics. It can also integrate with other analytical tools to provide contextual insights, such as correlating sentiment shifts with product updates or external events. The system ensures continuous monitoring, enabling real-time or periodic reporting to stakeholders. This approach enhances decision-making by providing a comprehensive view of sentiment evolution, helping organizations optimize their strategies throughout the object's lifecycle.
8. The computer program product of claim 5 , wherein the sentiment inflection point is a point in a lifecycle of the specified object when sentiments toward the specified object change.
This invention relates to analyzing sentiment trends over time for a specified object, such as a product, service, or brand, to identify key moments when public perception shifts. The system tracks sentiment data from various sources, such as social media, reviews, or surveys, and processes this data to detect inflection points—critical moments in the object's lifecycle where sentiment changes significantly. These inflection points may indicate turning points in public opinion, such as a sudden increase in positive sentiment after a product launch or a decline following a negative event. The system may use machine learning or statistical analysis to identify these shifts and correlate them with external factors, such as marketing campaigns, product updates, or competitive actions. By detecting these inflection points, businesses can make data-driven decisions to improve customer satisfaction, adjust strategies, or mitigate negative trends. The invention also includes visualizing sentiment trends and inflection points to provide actionable insights for stakeholders. This approach helps organizations understand how sentiment evolves over time and respond effectively to changes in public perception.
9. A system, comprising: a processor; and a computer readable storage medium having program instructions embodied therewith, the program instructions executable by the processor to cause the processor to: obtain information for a set of messages from online social networks related to a specified object, the information comprising at least a user identifier associated with each message, a time of each message, and content of each message; for each unique identifier, establish an initial message from the set of messages related to the specified object; analyze each message in the set of messages to determine a sentiment of each message toward the specified object; build a sentiment time line for each unique user identifier using the sentiment of each message toward the specified object; build a time-based sentiment model related to the specified object by overlapping the sentiment time lines for each unique user identifier according to the initial message for each unique user identifier; identify a sentiment inflection point in the time-based sentiment model, the sentiment inflection point representing a change in the sentiment toward the specified object; build a new sentiment time line for an additional unique user identifier; overlap the new sentiment time line with the time-based sentiment model according to an initial message for the additional unique user identifier; predict a change in the sentiment related to the specified object by the additional unique user identifier based on the new sentiment time line, the time-based sentiment model, and the sentiment inflection point; and generate a message targeting the predicted change in the sentiment related to the specified object by the additional unique user identifier.
This system analyzes social media messages to track and predict sentiment changes toward a specified object, such as a product or brand. The system collects messages from online social networks, extracting user identifiers, timestamps, and message content. For each user, it identifies their initial message about the object and analyzes the sentiment of all their subsequent messages to build a sentiment timeline. These timelines are then combined into a time-based sentiment model, which reveals sentiment inflection points—key moments where collective sentiment shifts. The system can integrate new users into this model by aligning their sentiment timeline with the existing model based on their initial message. By comparing the new user's sentiment trajectory with the model and inflection points, the system predicts future sentiment changes. Finally, it generates targeted messages designed to influence or respond to these predicted sentiment shifts. This approach enables real-time sentiment tracking and proactive engagement strategies based on historical and predictive sentiment analysis.
10. The system of claim 9 , wherein the time-based sentiment model relates to the specified object over a lifecycle of the specified object.
This invention relates to a system for analyzing sentiment data associated with a specified object over its lifecycle. The system includes a data processing module that collects and processes sentiment data from various sources, such as social media, reviews, or other digital platforms, to generate sentiment metrics. These metrics are analyzed using a time-based sentiment model that tracks changes in sentiment over the object's lifecycle, from its introduction to its eventual obsolescence or replacement. The model identifies trends, shifts, and patterns in sentiment, allowing for real-time or historical analysis of public perception. The system may also include a visualization module to present the sentiment data in graphical or tabular form, enabling users to monitor sentiment evolution and make data-driven decisions. The time-based sentiment model ensures that sentiment analysis is contextually relevant by considering the object's lifecycle stage, improving accuracy and actionable insights. This approach helps businesses, researchers, or other stakeholders understand how public opinion evolves over time, enabling better strategic planning and response to market dynamics.
11. The system of claim 9 , wherein the sentiment inflection point is a point in a lifecycle of the specified object when sentiments toward the specified object change.
This system monitors and analyzes sentiment trends related to a specified object, such as a product, service, or brand, over time. The system identifies a sentiment inflection point, which is a critical moment in the object's lifecycle when public or user sentiment shifts significantly. This shift may indicate a change in perception, such as increased satisfaction, dissatisfaction, or other emotional responses. The system tracks sentiment data from various sources, including social media, reviews, and user feedback, to detect these inflection points. By analyzing sentiment trends, the system helps stakeholders understand how public opinion evolves and identify key events or factors influencing these changes. The system may also correlate sentiment inflection points with external events, such as product launches, marketing campaigns, or competitive actions, to provide insights into their impact. This enables businesses to make data-driven decisions, adjust strategies, and improve user experiences based on real-time sentiment analysis. The system may include machine learning models to predict future sentiment trends and recommend actions to mitigate negative shifts or amplify positive ones.
Unknown
October 29, 2019
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